The area under the receiver operating characteristic curve was 0.81 for all patients, and 0.72 for the subset with at least one hypotensive blood pressure measurement. At a model threshold with sensitivity and specificity 0.74 and 0.74, respectively, the median advance detection time was 170.5 minutes (IQR 53 - 363).Septic Shock is a critical pathological state that affects patients entering the intensive care unit (ICU). Many studies have been directed to characterize and predict the onset of the septic shock, both in ICU and in the Emergency Department employing data extracted from the Electronic Health Records. Recently, machine learning algorithms have been successfully employed to help characterize septic shock in a more objective and automatic fashion. Only a few of these studies employ information contained in the continuously recorded vital signs such as electrocardiogram and arterial blood pressure. In particular, we have devised a novel feature estimation procedure able to consider instantaneous dynamics related to cardiovascular control. This work aims at developing a short-term prediction algorithm for identifying patients experiencing septic shock among a population of 100 septic patients extracted from the MIMIC-III clinical and waveform database. Among all the results obtained from several trained machine learning models, the best performance reached an AUC on the test set equal to 0.93 (Accuracy=0.85, Sensitivity=0.89 and Specificity=0.82).Heart diseases are the leading cause of death in developed countries. Ascertaining the etiology of cardiomyopathies is still a challenge. The objective of this study was to classify cardiomyopathy patients through cardio, respiratory and vascular variability analysis, considering the vascular activity as the input and output of the baroreflex response. Forty-one cardiomyopathy patients (CMP) classified as ischemic (ICM, 24 patients) and dilated (DCM, 17 patients) were analyzed. Thirty-nine elderly control subjects (CON) were used as reference. From the electrocardiographic, respiratory flow, and blood pressure signals, following temporal series were extracted beat-to-beat intervals (BBI), total respiratory cycle time series (TT), and end- systolic (SBP) and diastolic (DBP) blood pressure amplitudes, respectively. Three-dimensional representation of the cardiorespiratory and vascular activities was characterized geometrically, by fitting a polygon that contains 95% of data, and by statistical descriptive indices. The best classifiers were used to build support vector machine models. The optimal model to classify ICM versus DCM patients achieved 92.7% accuracy, 94.1% sensitivity, and 91.7% specificity. When comparing CMP patients and CON subjects, the best model achieved 86.2% accuracy, 82.9% sensitivity, and 89.7% specificity. https://www.selleckchem.com/ALK.html These results suggest a limited ability of cardiac and respiratory systems response to regulate the vascular variability in these patients.Non-invasive serial computed tomography coronary angiography (CTCA) was acquired from 32 patients and 3D reconstruction of 58 coronary arteries was achieved. The arterial geometries were utilized for blood flow and LDL transport modelling. Navier-Stokes and convection-diffusion equations were employed for simulation of blood flow and LDL transport, respectively. Disease progression was assessed comparing the follow-up and baseline arterial models after co-registration using side branches as anatomical landmarks. A machine learning model for predicting disease progression was built using the Gradient Boosted Trees (GBT) algorithm. The Accuracy, Sensitivity, Specificity and AUC of the developed methodology for predicting lumen area decrease equal was 0.68, 0.56, 0.34 and 0.59, respectively. The best results were found for the prediction of plaque area increase by 20%, with 0.73, 0.67, 0.86, and 0.76 accuracy, sensitivity, specificity andAUC, respectively. This approach outperforms significantly the predictive capability of models based on binary logistic regression.Persisting tachycardia is often observed in resuscitated septic shock patients, and it is an independent risk factor for increased mortality. Recently, several drugs, such as esmolol and ivabradine, have been proved to be beneficial in HR control, but their overall impact on cardiac functions needs further investigation. The aim of this study is to study the effects of the two drugs on heart function in a protocol of polymicrobial septic shock and resuscitation. Twelve pigs were divided into three experimental groups the esmolol-treated group (n=4), the ivabradine-treated group (n=5) and the control group (n=3). Cardiac autonomic activity was estimated by heart rate variability (HRV) indices and baroreflex sensitivity (BRS). The Buckberg index was adopted to evaluate myocardial oxygenation efficiency. Septic shock induced a severe autonomic dysfunction and a lower cardiac efficiency, not resolved by fluids resuscitation. The administration of the drugs improved both the HRV and the BRS, but this favourable condition was preserved after noradrenaline administration only in the esmolol group. The interaction of esmolol with the autonomic system is beneficial in septic shock to restore an improved condition of HRV and control, while ivabradine is not as effective when administered in adjunction to noradrenaline.Intervention in the early stages of cardiovascular and kidney diseases is proven to be more effective in preventing disease progression. Large artery stiffness measurement can be a potential early predictor of future risks. The purpose of the study reported in this work was to demonstrate the feasibility of our ARTSENS® Pen device as a high-throughput vascular screening tool for risk assessment. The study was performed during a medical camp conducted for awareness and early-stage detection of kidney diseases. Screening procedures included biosample tests and blood pressure measurements. Alongside, various clinically relevant measures of the arterial stiffness were evaluated using the ARTSENS® Pen, by measuring vessel wall dynamics via our proprietary image-free ultrasound algorithms. Stiffness measurement from the left common carotid artery on 85 participants could be completed within 4 hours, employing two units of ARTSENS® Pen; this also includes time taken for all the procedures enlisted in the study protocol.